Finally, we propose a lightweight measure, named Gaussian LEEP (\(\mathcal {N}\) LEEP), to rank checkpoints based on the recently proposed log expected empirical prediction (LEEP) [1]}. LEEP was originally designed to measure between-task transferabilities. It cannot handle the checkpoints pre-trained by unsupervised or self-supervised learning since it requires all checkpoints to have a classification head. Its computation cost could blow up when the classification head corresponds to a large output space. Moreover, it depends on the classification head's probabilistic output, which, unfortunately, is often overly confident [2]}.
